Natural Language Processing with Real-World Projects

Start your journey into advanced semantic processing with an introduction to fundamental concepts such as entities, arity, and reification. Learn about schemas and semantic associations, understanding how these elements form the backbone of semantic processing. The course covers important concepts like terms, the principle of composition, and tools like WordNet and word sense disambiguation, culminating in a case study using the Lesk algorithm. Move forward with an in-depth exploration of distributional semantics, delving into occurrence matrices and co-occurrence matrices to understand semantic relationships. Develop proficiency in creating and interpreting word vectors and grasp the importance of distance metrics. This section equips you with the skills needed to handle large sets of textual data, extracting meaningful semantic information. Finally, tackle advanced topics such as Latent Semantic Analysis (LSA) and Word2vec, applying these techniques to real-world scenarios through multiple case studies. By the end, you will have a robust understanding of advanced semantic processing techniques and their applications in NLP. This course is designed for NLP enthusiasts, data scientists, and professionals looking to deepen their knowledge of semantic processing. A basic understanding of NLP and machine learning concepts is recommended.

Begin your journey into Natural Language Processing (NLP) with an introduction to text data and encoding techniques, delving into the intricacies of regular expressions through extensive practice and use cases. Progress to lexical processing, learning to handle stopwords, split words, and implement bag-of-words and Tf-IDF models, applying these techniques to tasks like spam detection through detailed case studies. Advance to sophisticated lexical processing topics like spelling correction models and the Soundex algorithm, exploring practical implementations via Levenshtein Distance and spell correctors, and tackling challenges such as handling combined words like "New Delhi." This section solidifies your ability to preprocess and clean text data effectively. Transition to syntactic processing, covering parsing and grammar for English sentences, intermediate topics like stochastic parsing, the Viterbi algorithm, and Hidden Markov Models, reinforced through case studies and practical applications. Finally, tackle advanced syntactic processing techniques, including CFG grammar, top-down and bottom-up parsing, and probabilistic approaches like PCFG, concluding with a real-world project on information extraction through a comprehensive case study on ATIS flight reservations. Designed for aspiring NLP practitioners, data scientists, and software engineers, this course enhances understanding of syntactic processing, with a basic knowledge of Python programming and familiarity with machine learning concepts recommended.